US20170146608A1 - Method of dynamically extracting entropy of battery - Google Patents

Method of dynamically extracting entropy of battery Download PDF

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US20170146608A1
US20170146608A1 US15/044,400 US201615044400A US2017146608A1 US 20170146608 A1 US20170146608 A1 US 20170146608A1 US 201615044400 A US201615044400 A US 201615044400A US 2017146608 A1 US2017146608 A1 US 2017146608A1
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battery
entropy
soc
ocv
temperature
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Sang-Gug Lee
Guillaume Thenaisie
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Korea Advanced Institute of Science and Technology KAIST
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    • G01R31/3651—
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G01R31/361—
    • G01R31/3675—
    • G01R31/3679—
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3828—Arrangements for monitoring battery or accumulator variables, e.g. SoC using current integration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health

Definitions

  • the present invention relates to a battery, and more particularly to a method for measuring entropy of the battery dynamically.
  • the main solution to store electricity in all these devices is the battery even if sometimes small systems rely on hyper-capacitors as well.
  • the majority of currently used batteries are lithium-based batteries such as Li-Ion, Life-Po, etc. due to their higher power densities and fast charging abilities.
  • the lithium-based batteries have low self-discharge, and don't have any requirements for priming.
  • the lithium-based batteries are used to power a wide variety of consumer goods ranging from the mobile phones to children toys, e-bikes and passenger vehicles.
  • the lithium-based batteries are already the majority of the battery market, and demands for them are still increasing continuously, with an expectation of their markets to grow 4 times by 2020.
  • a hyper capacitor is emerging as a new way to store energy.
  • the hyper capacity provides a high energy density and thus can store almost as much electricity as the battery at a given weight, also having a long life.
  • the hyper capacitor is much faster and easier to charge, being safer in use, showing much lower resistance, and providing an excellent low-temperature charge and discharge performance.
  • the hyper capacitor has high self-discharge, low cell energy and a linear discharge voltage, which prevent it from using the full energy spectrum. Due to these disadvantages, the hyper capacitor fails to take a main position in the market.
  • the lithium-based batteries still dominate the market and such a situation will continue for a long time.
  • the lithium-based batteries also face some challenges. They are not as robust as some other rechargeable technologies. They require protection from being over charged and discharged too far. Also, they are sensitive to temperature and misuses of voltage and current. If proper conditions are not satisfied, their life will degrade easily.
  • BMS battery management system
  • the main point in the development of the BMS is that it is performed by electrical and computer science engineers, who base their approach on empirical analysis and electrical modeling of the behavior of the batteries.
  • the circuit shown in FIG. 3 represents an electrical modeling of a lithium-ion battery.
  • Such methods provide the advantage of quick development, easy to embed solution and linear industrial development process (Chemists create battery, while electrical engineers and computer science engineers develop hardware, and algorithms and controls, respectively).
  • the present invention has been made under the recognition of the above-mentioned problems of the conventional art to overcome its limitations. It is an object of the present invention to provide a method to extract the entropy values of a battery in real-time during the battery's charge and discharge.
  • SOH state of health
  • SOS state of safety
  • a remaining capacity (SOC) of a battery is estimated with a BMS. Then, the estimated SOC value is compared for equality with a measurement reference value. If not equal, the SOC estimation is performed again.
  • SOC value is equal to the measurement reference value
  • OCV open circuit voltage
  • the data of temperature measurement and OCV estimation are stored. Based on the newly stored data of the temperature measurement and the OCV estimation calculated is entropy of a current state of the battery. Based on the newly obtained entropy value, a state of health (SOH) value and a state of safety (SOS) value of the battery are update.
  • the conventional BMS estimates SOH through the battery internal resistance without use of entropy, but the present invention can thermodynamically and analytically grasp the internal state of the battery by using entropy. Therefore, SOH as well as SOS can be monitored, thereby being able to know a more accurate battery status.
  • the dynamic estimation method of battery entropy being a method to be performed by executing a program in a BMS connected to a battery, may include a step of measuring temperature of the battery of which functional status is situated in a dynamically varying state and estimating an OCV of the battery around a temperature measurement time, and a step of estimating an entropy change amount of the battery based on the temperature measurement value and the OCV estimation value.
  • the dynamic estimation method of the battery entropy may further include a step of estimating SOC of the battery while continuously monitoring the SOC and comparing an SOC estimation value with a preset measurement reference value to determine whether the SOC estimation value is equal to the preset measurement reference value.
  • the OCV estimation step and the entropy change estimation step may be carried out.
  • the SOC estimation value may be calculated by linear regression analysis of a residual charge amount of the battery based on a predetermined battery temperature and the OCV of the battery.
  • the SOC estimation value may be calculated by the coulomb counting method for the measuring the current of the battery and integrated with respect to time them.
  • the SOC estimation value may be calculated using the Kalman filtering.
  • the measurement reference value as a reference for estimating the entropy change amount may be set optionally depending on the need.
  • the dynamic estimation of the battery entropy, based on the entropy variation, the battery health state (State of Health: SOH) and/or safety conditions indicate a risk of the battery (State of Safety: SOS) a step of calculating the value may be further included.
  • the measure of the entropy variation, the SOC and the battery temperature, and the OCV can be measured by the measurement on the basis of the correlation between the OCV and the battery temperature is obtained by performing over a period of at least 2 cycles.
  • the amount of change of the entropy is measured, it can be performed over the whole period of the SOC.
  • the measured amount of change of the entropy is, the SOC has to be performed repeatedly each time change as the measurement reference value.
  • the OCV is independent of the measurement, it can be calculated by using the estimated SOC estimated value and the battery temperature.
  • the dynamic estimation of the battery entropy method, the estimated value of the OCV with the temperature measurement value may further comprise the step of storing in a database in the BMS.
  • the method of estimating the dynamic battery entropy can be implemented in integrated circuit systems.
  • the method of estimating the dynamic battery entropy can be implemented as a program running on a general purpose CPU or MCU.
  • the method of estimating the dynamic battery entropy may be implemented as a logic circuit.
  • the method of estimating the dynamic battery entropy may be implemented as a program running on the cloud system.
  • the present invention can dynamically estimate the entropy of the battery while charging or discharging the battery.
  • the entropy estimation may also be made very quickly. This advantage extremely raises practicality of the present invention.
  • FIG. 1 is a circuit diagram by electrically modeling a lithium-ion battery in accordance with conventional methods for ion battery;
  • FIG. 2 is a graph illustrating a schedule for measuring SOC of a battery by a static method in the process of charging the battery;
  • FIG. 3 is a flow chart illustrating a method for dynamically estimating the entropy of the battery according to the present invention
  • FIG. 4 illustrates an example of the BMS for carrying out the method proposed by the present invention
  • FIG. 5 illustrates another example of the BMS for carrying out the method proposed by the present invention
  • FIG. 6 illustrates further another example of the BMS for carrying out the method proposed by the present invention for the application to a remote cloud computing system
  • FIG. 7 illustrates an example of a characteristic curve, provided in any data sheet of the Li-ion battery cell, showing the relationship between a battery capacity and an OCV of the battery;
  • FIG. 8 is a graph showing illustratively the battery voltages for different discharge current values as a function of the battery SOC
  • FIG. 9 illustrates an equivalent circuit diagram of the battery modeled in a form consisting of a voltage generator for generating an electromotive force and an internal resistance
  • FIG. 10 is an exemplary graph illustrating entropy change as a function of the SOC
  • FIG. 11 is an exemplary graph illustrating the relationship between the change in entropy and the battery degradation.
  • FIG. 12 is an exemplary graph illustrating the relationship between the self-heating rate and the change in entropy.
  • Open circuit voltage Voltage between an anode and a cathode of a battery cell when no load is connected to the battery cell, that is, no current flows out from the battery cell. Theoretically, the maximum value of the OCV becomes equal to the value of the electromotive force of the battery cell.
  • Electrode Cell A device for storing chemical energy that can be converted into electrical energy, usually in the form of direct current.
  • Battery A device containing one or a group of cells to store the electrical energy.
  • SOC State of Charge
  • SOH State of health
  • SOH is a figure of merit of the condition of a battery (or a cell, or a battery pack), compared to its ideal conditions.
  • a battery's SOH will be 100% at the time of manufacture and will decrease over time and use.
  • a battery's performance at the time of manufacture may not meet its specifications, in which case its initial SOH will be less than 100%.
  • SoS State of Safety
  • Battery Management System Any electronic system that manages a rechargeable battery (cell or battery pack), such as but not limited to, protecting the battery from operating outside its safe operating area, monitoring its state, calculating secondary data, reporting that data, controlling its environment, authenticating it and/or balancing it.
  • Enthalpy A thermodynamic quantity equivalent to the total heat content of a system. It is equal to the internal energy of the system plus the product of pressure and volume. The change in enthalpy of a system is associated with a particular chemical process.
  • Entropy A thermodynamic quantity representing the unavailability of a system's thermal energy for conversion into a mechanical work, often interpreted as the degree of disorder or randomness in the system.
  • Battery cycle A part of the battery life composed of a discharge and a charge.
  • Li-based battery All batteries whose chemistry relies on lithium as one of the two RedOx couples are considered as the lithium-based battery. It envisioned, but is not limited to, Li-Ion, Li—Po, Li—Mn, Li—Al, etc.
  • ETMs electrochemical thermodynamics measurements
  • G represents the Gibb's free energy
  • n denotes the amount of electron exchange in the conventional basic reaction
  • F is the Faraday constant
  • the entropy ⁇ S(x) and the enthalpy ⁇ H(r) in Equations (1) and (3) are measured at a defined state of charge of the battery, ‘x’, the entropy ⁇ S(x) and the enthalpy ⁇ H(x) can be defined as the local slope of the battery system' total entropy and the total enthalpy variation vs. ‘x’, respectively. Accordingly, there is no need for a reference state to determine the entropy ⁇ S(x) and the enthalpy ⁇ H(x).
  • the entropy is then determined as the constant coefficient linking the temperature difference and the OCV difference between two measurement points.
  • the entropy displays a fixed value for a given SOC, and the relationship between the OCV and the temperature is linear.
  • this method is a kind of static measurement method in that as can be know from the measurement schedule illustrated in FIG. 4 , the battery charging must be stopped until the battery can transit from a charging state to the chemical relaxation state at every measurement interval of SOC, that is, during a time interval between an OCV measurement at a specific value of SOC and a next OCV measurement at a next specific value of SOC (the measurement of OCV may be performed, for example, at every 5% of SOC). That is, there is a consequent delay between two consecutive measurements of OCV, as the battery must relax from the charge, then it must reach the thermo-chemical equilibrium before the OCV is measured again.
  • this static method is non-applicable for real-life systems. Indeed, no embedded system can afford to turn off whenever it needs to update its battery's SOC, SOH or SOS. Moreover, the simple relaxation time makes the charge of a battery a day-long process. This is unrealistic for applications where the main trend is to reach 60% of the full charge within 30 minutes. Thus, the approach applicability is stopped to the laboratory measurements instead of real-time system use. Moreover, due to the need to cool down (or heat-up) the conventional entropy extraction method makes such a BMS expensive, costly and hardly applicable to the real-life systems, especially the small IoT and smartphone devices as the volume and the unit cost is too reduced for a cooling system to be an option for any company.
  • FIG. 5 is a flowchart illustrating an algorithm of the entropy extraction method proposed by the present invention. This algorithm may be implemented as a part of the functions of BMS.
  • the BMS prevents the battery from being operated outside of a safe operation area, and manages the battery with checking necessary matters by monitoring a state of the battery, calculating secondary data, reporting the data, controlling environments of the battery, performing the battery authentication, etc.
  • the BMS for the application of the present invention may be a circuit board type BMS 100 that on a circuit board installed are a micro-controller (or a CPU and a memory) for performing required operations and controls by running programs and storing relevant data, a probe 120 that is connected to the battery and acquires necessary signals to be provided to the micro-controller 110 , and a power IC 130 for controlling the battery-driven devices to consume less power.
  • a micro-controller or a CPU and a memory
  • a probe 120 that is connected to the battery and acquires necessary signals to be provided to the micro-controller 110
  • a power IC 130 for controlling the battery-driven devices to consume less power.
  • the BMS for the application of the present invention may be an integrated circuit type BMS 200 implemented with a logic controller 210 and a memory 220 that have a function equivalent to that of the micro-controller 110 , a power switch 230 and a power driver 240 (this drives the power switch 230 according to the control of the logic controller 210 ) that have a function equivalent to that of the power IC 130 , and a probe 250 .
  • the BMS features of the present invention may act in conjunction with a cloud computing system. That is, the BMS apparatus for this is, as illustrated in FIG. 8 , may include a network interface 320 for interfacing communications with a remote cloud system, and a power switch 330 and a power driver 340 , and a probe 350 as mentioned above.
  • the hardware configuration of the BMS applicable to the present invention may vary. Any type of BMS may carry out the functions described below in connection with the battery if it can perform required computations and controls through running relevant programs and other operations such as data storing.
  • the method of the present invention can be carried out while the battery is being charged or discharged.
  • the SOC value is changed as the battery is charged or discharged. That is, during that time the state of battery may be dynamically changed.
  • the time interval period to extract the entropy of the battery may be set based on the change in the SOC value. For example, it may be programmed that at every 5% change of the SOC relative to the SOC value of fully charged battery a loop for estimating the entropy through the measurements of temperature and OCV should be performed.
  • the SOC estimating time interval may be set to other values depending on the needs of the system, such as, for example, 1%, 3% or 8%.
  • the algorithm for the BMS to extract the entropy of the battery according to the present invention is as follows.
  • the BMS monitors a charged level of the battery, that is, the change in the SOC while continuing to measure the SOC, in the process of charging or discharging the battery (Step S 20 ).
  • the SOC may be measured by an indirect way since it is difficult to measure the SOC directly.
  • a method for measuring the SOC is to estimate it by a linear regression method on the basis of the OCV and temperature of the battery. Since a voltage of the battery is affected by temperature, the SOC can be calculated with reference to the voltage and temperature of the battery.
  • the battery manufacturers provide a data sheet representing the characteristic of the battery for each battery.
  • the battery data sheet usually contains a characteristic curve of the battery, and it is possible to determine an actual charge state (SOC) of the battery from the OCV and temperature of the battery on the basis of the characteristic curve of the battery.
  • FIG. 9 illustrates a characteristic curve showing the relationship between the battery capacity and the OCV that is provided in a data sheet, for example, of 2200 mAh Li-Ion battery cell.
  • the battery voltage is gradually changed in accordance with the remaining charge amount in the battery.
  • the remaining charge amount in the battery may be estimated by a linear regression estimation using the OCV value and its corresponding temperature measurement value, and battery characteristic curve.
  • Another method for estimating the SOC of the battery is a method of using a Coulomb counting.
  • the Coulomb counting method being a fundamentally different approach than the OCV based method, is known as a current integration method.
  • the method calculates the SOC by measuring a battery current and integrating it in time. Instead of considering the potential energy of a known-capacity battery and determining the percentage of charge remaining in it, the method considers the battery as a fuel tank. Hence by measuring the quantity of charge entering the battery during a battery charging process, the method determines the maximum capacity of the battery. Then, by counting the charge flowing out of the battery, the remaining capacity of the battery can be easily determined.
  • the quantity of charge going in or out of the battery is determined by the integral over time interval of the current flowing in or out of the battery, hence named ‘Coulomb counting’.
  • This hybrid type method may be used in a manner that one of the two OCV estimation methods makes the other method's error to be reduced.
  • the BMS monitors the change in the SOC values while measuring periodically the SOC of the battery, using any one of the methods mentioned above. And, whenever the SOC value is calculated, the BMS determines whether the measured SOC value reaches a predetermined value for the entropy extraction (Step S 32 ). For example, if the SOC measurement period is set to 5%, at every 5% increase or decrease of the SOC value compared to that of the previous measurement period the entropy extraction loop (Steps S 34 -S 40 ) that will be described below may be carried out. In other cases, it is returned back to the step S 30 to continue to monitor the change in the SOC value.
  • the BMS measures a battery temperature at that time right away. And at the same time, the BMS estimates the OCV of the battery (step S 34 ). Since the OCV is the open circuit voltage of the battery, directly measuring its dynamic variation does not make sense, even hardly impossible in reality. Therefore the OCV is measured indirectly, i.e., estimated.
  • a battery temperature may be measured, for example, in the Celsius unit, and the OCV may be measured, for example, in volts.
  • step S 34 several methods may be used for the OCV estimation of the battery.
  • An exemplary method to estimate the OCV is, as mentioned in the description of the OCV estimation, a method of using the characteristic curve of the battery.
  • a data sheet of the battery can be obtained from the battery manufacturer.
  • the data sheet provides the technical specifications of the battery (for example, an operating range, safety working conditions, a size of the package, etc).
  • Most battery data sheet includes information of the characteristic curve representing the relationship between the battery voltage (OCV) and the discharge capacity (SOC).
  • the characteristic curve presents, for example, the battery voltage for different values of the discharge current as a function of the battery SOC.
  • FIG. 10 shows an exemplary graph thereof.
  • both voltage and current are measured at the terminals of the battery. Then, selected are two curves that discharge current representing the measured current. And by using the two selected curves, values of the OCV can be estimated based on linear regression method.
  • Another method for estimating the OCV is to represent the battery in a simplified model, when the batteries are exposed to low frequency charge (discharge) variations and its drain (charge) current is not too high (usually 20%° or less of the rated current).
  • the battery connected to a load (R) can be modeled in the form consisting of a voltage generator for generating an electromotive force (E) and an internal resistance (r). From the equation of ohm, a voltage drop occurring inside the battery can be determined as an effect of current that flows through the internal resistance.
  • the electromotive force (e) corresponds to the OCV, and the voltage appearing across both electrodes of the battery is the same as the electromotive force (e), that is, the OCV when no current (I) flows.
  • the voltage appearing across both terminals (A, B) of the battery is equal to the sum of the electromotive force (c) and the voltage drop in the internal resistance (r). This can be represented as the following equations.
  • V _batt I ⁇ 0 ⁇ +r ⁇ I (4-3)
  • the OCV estimation is also possible by Kalman filtering as another method.
  • the Kalman filtering is an algebraic iterative method used in many domains when one has to estimate precisely the value of a state variable but can only measure its effects or derivate signals.
  • the method is fairly simple in concept but can be challenging to apply as an algorithm. Its concept is as follow: (i) A system for it is filled with the previous estimated state of the variables; (ii) From measurement, the previous estimated state and a custom model of the system, the next state is estimated, then from the state an estimation is made over a measurable parameter value; (iii) The parameter is then measured, and the estimation error (measure .vs.
  • estimation is computed; (iv) From the estimation error the estimated state is corrected and used to feed an input of the system for the next step.
  • This method follows a step by step process. Its precision depends on the model on which it relies and on the estimation temporal step compared to the variation speed of the system under surveillance.
  • the battery temperature may be also measured along with the OCV estimation (Step S 34 ).
  • the battery temperature can be directly measured in real time by using a temperature sensor. In some cases, the temperature may be indirectly measured, or an approximated value of the temperature may be used based on the room temperature or weather information where the battery exists.
  • step S 32 It is needed to measure the entropy over the full range of the SOC in order to determine the SOH and SOS. Therefore, this point should be considered in determining the resolution of SOC setting value in step S 32 that is used as a reference point for measuring the temperature and the OCV of the battery.
  • the battery temperature and OCV measurements may be carried out over several cycles at a specific SOC value.
  • the number of the measurements may be determined in consideration of the precision expected and the entropy usual evolution rate of a normal system. For example, it may vary between 2 and the number as much as the user wants.
  • the temperature value measured and the OCV value estimated in step S 34 is stored in a database of the storage means within the BMS (step S 36 ).
  • step S 38 operations for calculating the entropy of the battery may be conducted.
  • the entropy calculation may be done by using the following equation.
  • a variation of entropy newly measured at a specific SOC value ( ⁇ S new : the difference between the entropy estimated in the previous cycle and the entropy estimated at the current period) is proportion to a value obtained by dividing a variation of the estimated OCV value ( ⁇ OCV estimated : a difference between the estimated OCV value of the previous cycle and the estimated OCV value of the current cycle) by a variation of the measured temperature value ( ⁇ T measured : a difference between the measured temperature value of the previous cycle and the measured temperature value of the current cycle).
  • ⁇ S new the difference between the entropy estimated in the previous cycle and the entropy estimated at the current period
  • Gibb's energy represents the amount of ‘useable’ energy in a chemical system. In the case of a battery, this energy can be translated into electricity. Hence the Gibb's energy is the quantification in Joules of the charge present in a battery at defined instant times the voltage of the battery at that very moment.
  • the Gibb's energy is, in the case of a battery, determined by the following equation.
  • x represents the percentage of the chemical reaction done, hence the amount of charge remaining, n being the amount of electron exchange in the typical elementary reaction, and F being the Faraday constant.
  • enthalpy H is the algebraic representation of the total amount of energy in the system, that is, sum of the useable one and the unusable one (potential energy, kinetic energy if any).
  • the system can be reduced to a thermos-chemical analysis.
  • the entropy variation ⁇ S can be extracted from the differential value of the OCV over the battery temperature T as shown in equation (5). Like this, if extraction of entropy variations is done at every measurement cycle, the full entropy profile until the battery is fully charged can be obtained.
  • the entropy value may be updated to estimate the SOH and SOS, and the SOH and SOS can be determined based on the variation of the entropy ⁇ S (step S 40 ). Therefore, the battery's state functions SOH and SOS can be computed from the measurement around specific points through the calculus of differential entropy, with no need for any continuous monitoring.
  • the entropy of the battery does not evolve over battery's aging homogeneously over the entire range of the SOC.
  • the variation on the entropy in these values is substantially proportional to the battery capacity (refer to the graph shown in FIG. 13 ) and the self-heating rate (refer to the graph shown in FIG. 14 ).
  • the differential entropy would be a perfect tool to estimate the SOH through a reference equation (which can be obtained in laboratory prior to the implementation of the BMS).
  • the self-heating rate is a chemical state function that determines the thermal runaway capability of the battery. Here it means the probability that the battery will take fire spontaneously within the safety operation limits. Hence it provides the SOS.
  • the present invention provides a method to extract entropy estimation from the battery that is servicing any work.
  • rechargeable secondary batteries have received spotlight and among them the lithium-based battery is most commonly used.
  • the present invention can be applied to the BMS employed by all the devices using the lithium-based battery.
  • the present invention may be applied in a form of block to the BMS.
  • the block added may be implemented in software and/or hardware.
  • the BMS may be designed such that it can be associated with the battery in the system at a remote location via an interface to exert the functions of the present invention.
  • the present invention is applicable to a BMS for a variety of secondary batteries, including a lithium-based battery. It is also applicable to the wearable devices, electric vehicles, and portable devices.

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